Convolutional codes are often used in digital communication systems to protect transmitted information from error. At the transmitter, an outgoing code vector may be described using a trellis diagram whose complexity is determined by the constraint length of the encoder. Although computational complexity increases with increasing constraint length, the robustness of the coding also increases with constraint length.
At the receiver, a practical soft-decision decoder, such as a Viterbi decoder as is known in the art, uses a trellis structure to perform an optimum search for the maximum likelihood transmitted code vector. The Viterbi algorithm, however, is computationally complex, and its complexity increases exponentially with increasing constraint length. This essentially means that a Viterbi decoder requires a significant amount of memory and processing power for convolutional codes with large constraint lengths.
Coders for various communications systems, such as Direct Sequence Code Division Multiple Access (DS-CDMA) standard IS-95 and Global System for Mobile Communications (GSM), have such large constraint lengths. For example, the GSM half-rate constraint length K=7 and the IS-95 constraint length K=9.
Another disadvantage of Viterbi decoders is that a fixed number of computations must be performed for each code vector, irrespective of the actual number of errors that occurred during transmission. Thus, a Viterbi decoder processes a received signal having few transmission errors or no errors at all using the same number of computations as a received signal having many errors.
More recently, turbo codes have been developed that outperform conventional coding techniques. Turbo codes are generally composed of two or more convolutional codes and turbo interleavers. Turbo decoding is iterative and uses a soft output decoder to decode the individual convolutional codes. The soft output decoder provides information on each bit position which helps the soft output decoder decode the other convolutional codes. The soft output decoder is usually a MAP (maximum a posteriori) decoder which requires backward and forward decoding to determine the soft output. However, because of memory, processing, and numerical tradeoffs, MAP decoding is usually limited to a sub-optimal approximation. All of these variants require both forward and backward decoding over the block.
For future standards, such as the 3GPP (third generation partnership project for wireless systems), an 8-state turbo code with a block length of N=5120, needs 40960 words of intermediate storage which may be unacceptable. Future systems (larger frame and greater number of states) will require even more memory. By comparison, a Viterbi decoder that does not produce soft outputs for an N=5120, 8-state trellis requires less than 100 words of intermediate.
There is a need for a soft output decoder that reduces overall memory and processing requirements for decoding convolutional codes without the degree of limitations imposed by prior art turbo and MAP decoders.